CN110418603B

CN110418603B - Blood pressure data processing device, blood pressure data processing method, and program

Info

Publication number: CN110418603B
Application number: CN201880017870.6A
Authority: CN
Inventors: 小久保绫子; 和田洋贵; 中嶋宏
Original assignee: Omron Healthcare Co Ltd
Current assignee: Omron Healthcare Co Ltd
Priority date: 2017-03-14
Filing date: 2018-03-12
Publication date: 2022-06-10
Anticipated expiration: 2038-03-12
Also published as: US20200008690A1; WO2018168810A1; JP6790936B2; JP2018149183A; CN110418603A; DE112018001399T5

Abstract

A blood pressure data processing device detects a sudden increase in blood pressure from time-series data of blood pressure values, and is provided with: an acquisition unit for acquiring time series data of blood pressure values; a calculation unit that sets one or more peak detection sections in the time series data and calculates a feature value based on any one of systolic blood pressure, diastolic blood pressure, and pulse pressure for each peak detection section; and a determination unit that determines at least one first peak value from the feature value of each peak detection section.

Description

Blood pressure data processing device, blood pressure data processing method, and program

Technical Field

The present invention relates to a technique for processing blood pressure data obtained from a blood pressure measurement device for measuring the blood pressure of a subject.

Background

In a patient with Sleep Apnea Syndrome (SAS), it is known that blood pressure rises rapidly at the time of restarting breathing after Apnea and then falls.

Blood Pressure measurement (ABPM; infant Blood Pressure Mons) is performed in 24 hours, and Blood Pressure values are measured at several points every 1 hour. In such ABPM, it is not possible to capture blood pressure fluctuations occurring in a short time, and it is difficult to detect rapid blood pressure fluctuations, i.e., rapid increases.

Jp 2007-282668 a describes that blood pressure value data measured on a plurality of dates and times is integrated by a conventional blood pressure measurement device for the purpose of capturing the daily or weekly fluctuation of a blood pressure measurement value.

Jp 2012-239807 a describes a method of evaluating a cardiovascular risk of a subject based on a relationship between a blood pressure measured in a hypoxic state and a blood oxygen saturation level, and a method of determining a difference (an increase amount of the blood pressure) between a blood pressure measured in a hypoxic state and a blood pressure measured in a non-hypoxic state.

Disclosure of Invention

However, no technique for detecting a surge based on blood pressure value data obtained from a blood pressure measurement device has been established. Therefore, in order to obtain blood pressure information related to the surge, it is necessary to perform an operation by a person such as a doctor. The amount of time-series data regarding blood pressure values obtained by a patient during sleep is enormous. For example, if the sleep time for one night is set to 8 hours, time series data of blood pressure values of about 3 ten thousand pulses can be obtained. It is difficult to manually find the surge from such blood pressure data.

The present invention has been made in view of the above circumstances, and an object thereof is to provide a blood pressure data processing device, a blood pressure data processing method, and a program that can detect a rapid increase in blood pressure from time-series data of blood pressure values.

In order to achieve the above object, the present invention employs the following embodiments.

In a first embodiment, a blood pressure data processing device includes: an acquisition unit for acquiring time series data of blood pressure values; a calculation unit that sets one or more peak detection sections in the time series data and calculates a feature value based on any one of systolic blood pressure, diastolic blood pressure, and pulse pressure for each peak detection section; and a determination unit that determines at least one first peak from the feature value of each peak detection section.

According to the first embodiment, the first peak can be specified from the feature value of any one of the systolic blood pressure, diastolic blood pressure, and pulse pressure for each peak detection section in the time series data based on the blood pressure value. Therefore, a rapid increase in blood pressure can be detected as the first peak. If the time-series data is set to the blood pressure value of each pulse, the rapid increase of the blood pressure can be detected with high precision. In addition, it is possible to stably detect a blood pressure surge having various patterns, which does not occur in a certain period.

In the second embodiment, the characteristic amount may be a maximum value of any one of the systolic blood pressure, the diastolic blood pressure, and the pulse pressure.

In the third embodiment, the characteristic value may be a difference between a maximum value of any one of the systolic blood pressure, diastolic blood pressure, and pulse pressure in the peak detection section and a minimum value of any one of the systolic blood pressure, diastolic blood pressure, and pulse pressure at a time point before the maximum value in the peak detection section. According to the third embodiment, a rapid increase in blood pressure with a rapidly increasing blood pressure value can be detected based on the fluctuation amount of any one of the systolic blood pressure, the diastolic blood pressure, and the pulse pressure in the peak detection section.

In the fourth embodiment, the present invention may further include an extraction unit that extracts a peak candidate for each of the peak detection sections by applying a determination criterion to the feature amount.

In the fifth embodiment, the peak candidate may include a time point at which the maximum value satisfying the determination criterion is obtained, and the determination unit may determine the first peak based on a predetermined number or more of the peak candidates at the same time point. According to the fifth embodiment, it is possible to detect a sudden increase in blood pressure by combining peak candidates indicated by the time points at which the maximum values satisfying the determination criterion are obtained.

In a sixth embodiment, the determination unit performs the screening of the first peak value based on another feature amount based on at least one of a waveform shape, time information, and frequency information of the time series data. According to the sixth embodiment, it is possible to reliably detect a case in which an image is more like a surge while preventing an increase in peak data.

In the seventh embodiment, the other feature values may be set as a rise time, a fall time, an area, and a correlation coefficient of a rapid increase in blood pressure.

An eighth embodiment is the blood pressure data processing device according to the first to seventh embodiments, further comprising a display unit for displaying the time series data together with the first peak value.

In the ninth embodiment, the search unit may be further provided with a search unit that detects at least one second peak by searching for a maximum value of the time series data at least one time point before and after a search range including the first peak.

According to the ninth embodiment, more peaks can be detected by searching for the maximum value of the time series data than in the case where only the first peak is specified. Further, according to the fifth embodiment, it is possible to detect a sudden increase in blood pressure that is a second peak at a time point before the first peak or a sudden increase in blood pressure that is a second peak at a time point after the first peak.

In the tenth embodiment, the present invention may further include: a display section for displaying the time series data together with the first peak value and the second peak value; and a display control unit that controls the display unit to display the first peak and the second peak in a differentiated manner. According to the tenth embodiment, it is possible to cope with: the intention of the user to confirm the detection result of the peak value generated over a long period of time, that is, the long blood pressure surge; and the intention of the user to confirm a detailed detection result of the peak value, that is, the intention of the blood pressure surge occurring before and after the long blood pressure surge and detected by the search; both of which are described below.

According to the present invention, a technique capable of detecting a sudden increase in blood pressure from time-series data of blood pressure values can be provided.

Drawings

Fig. 1 is a block diagram showing a blood pressure data processing device according to a first embodiment.

Fig. 2 is a block diagram showing an example of the blood pressure measuring apparatus shown in fig. 1.

Fig. 3 is a side view showing the blood pressure measuring unit shown in fig. 2.

Fig. 4 is a sectional view showing the blood pressure measuring unit shown in fig. 2.

Fig. 5 is a plan view showing the blood pressure measuring unit shown in fig. 2.

Fig. 6 is a diagram showing waveforms of pressures measured by the respective pressure sensors shown in fig. 5.

Fig. 7 is a diagram showing an example of the sliding window.

Fig. 8 is a flowchart showing an example of a processing procedure for outputting data of the first peak.

Fig. 9 is a diagram showing an example of removing spike noise.

Fig. 10 is a diagram showing an example of removing large variation noise.

Fig. 11 is a flowchart illustrating in detail the repetition process illustrated in fig. 8.

Fig. 12 is a diagram showing a result of detection of a sudden increase in blood pressure by the blood pressure data processing device according to the first embodiment.

Fig. 13 is a block diagram showing a blood pressure data processing device according to a second embodiment.

Fig. 14 is a flowchart showing an example of a processing procedure for outputting data of the second peak.

Fig. 15A is a graph of the surge generated over a shorter period of time.

Fig. 15B is a diagram showing an example of a surge generated over a long period of time.

Fig. 16 is a diagram showing an example of detection omission of surge.

Fig. 17A is a diagram showing a search for the maximum local maximum value at the time point before the excitation point.

Fig. 17B is a diagram showing a search for the maximum local maximum value at a time point after the excitation point.

Fig. 18 is a block diagram showing a blood pressure data processing device according to a third embodiment.

Fig. 19 is a diagram showing an example of display by the visualization unit.

Fig. 20 is a diagram showing an example of a visualization file (file) output from the visualization unit.

Fig. 21 is a block diagram showing an example of the hardware configuration of the blood pressure data processing device.

Detailed Description

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following embodiments, the same reference numerals are given to the same components, and redundant description thereof will be omitted.

(first embodiment)

Fig. 1 schematically shows a blood pressure data processing device 10 according to a first embodiment of the present invention. As shown in fig. 1, a blood pressure data processing apparatus 10 is an apparatus that processes time series data 11 of blood pressure values obtained from a blood pressure measurement apparatus 20 for measuring the blood pressure of a subject. The blood pressure data processing device 10 can be mounted on a computer such as a personal computer or a server.

First, the blood pressure measurement device 20 will be described with reference to fig. 2 to 6. In the first embodiment, the blood pressure measurement device 20 is a wearable device worn on the wrist of the subject, and measures the pressure pulse wave of the radial artery of the subject by a tonometry method. Here, the tonometry is a method of forming a flattened portion in an artery by pressing the artery with an appropriate pressure on the skin, and measuring a pressure pulse wave non-invasively with a pressure sensor in a state where the balance between the inside and the outside of the artery is maintained. The blood pressure value per one pulse can be obtained by the tonometry.

Fig. 2 schematically shows a blood pressure measurement device 20 according to a first embodiment. As shown in fig. 2, the blood pressure measurement device 20 includes a blood pressure measurement unit 21, an acceleration sensor 24, a storage unit 25, an input unit 26, an output unit 27, and a control unit 28. The control unit 28 controls each unit of the blood pressure measurement device 20. The function of the control Unit 28 can be realized by a processor such as a CPU (Central Processing Unit) executing a control program stored in a computer-readable storage medium such as a ROM (Read-Only Memory).

The blood pressure measurement unit 21 measures a pressure pulse wave of the subject and generates blood pressure data including a measurement result of the pressure pulse wave. Fig. 3 is a side view showing a state where the blood pressure measuring unit 21 is worn on the wrist Wr of the measurement subject by a belt (belt), not shown, and fig. 4 is a cross-sectional view schematically showing the structure of the blood pressure measuring unit 21. As shown in fig. 3 and 4, the blood pressure measurement unit 21 includes a sensor unit 22 and a pressing mechanism 23. The sensor portion 22 is disposed in contact with a site (in this example, the wrist Wr) having the radial artery RA inside. The pressing mechanism 23 is used to press the sensor unit 22 against the wrist Wr.

Fig. 5 shows a surface of the sensor unit 22 on the side contacting the wrist Wr. As shown in fig. 5, the sensor unit 22 includes one or more (two in this example) pressure sensor arrays 221, and each of the pressure sensor arrays 221 includes a plurality (for example, 46) of pressure sensors 222 arranged along the direction B. The direction B is a direction intersecting the extending direction a of the radial artery in a state where the blood pressure measurement device 20 is worn on the subject. A channel number is assigned to the pressure sensor 222. The configuration of the pressure sensor 222 is not limited to the example shown in fig. 5.

Each pressure sensor 222 measures pressure and generates pressure data. As the pressure sensor, a piezoelectric element for converting pressure into an electric signal may be employed. The sampling frequency is, for example, 125 Hz. A pressure waveform as shown in fig. 6 can be obtained as pressure data. The measurement of the pressure pulse wave is generated based on pressure data output from one of the pressure sensors (active channel) 222 adaptively selected among the pressure sensors 222. The maximum value of the waveform of the Pressure pulse wave of the primary pulse component corresponds to the Systolic Blood Pressure (SBP), and the minimum value of the waveform of the Pressure pulse wave of the primary pulse component corresponds to the Diastolic Blood Pressure (DBP). The blood pressure data may include the measurement of the pressure pulse wave, and the pressure data output from each pressure sensor 222. The measurement result of the pressure pulse wave may be generated not by the blood pressure measurement device 20 but by the blood pressure data processing device 10 based on the pressure data.

The pressing mechanism 23 includes, for example, an air bag and a pump for adjusting the internal pressure of the air bag. When the controller 28 drives the pump to increase the internal pressure of the air bladder, the pressure sensor 222 is pressed against the wrist Wr by the expansion of the air bladder. The pressing mechanism 23 is not limited to the structure using the air bladder, and may be implemented by any structure capable of adjusting the force for pressing the pressure sensor 222 against the wrist Wr.

The acceleration sensor 24 detects acceleration acting on the blood pressure measuring device 20 and generates acceleration data. As the acceleration sensor 24, for example, a three-axis acceleration sensor can be used. The detection of the acceleration is performed in parallel with the measurement of the blood pressure.

The storage section 25 includes a computer-readable storage medium. For example, the storage unit 25 includes a ROM, a RAM (Random Access Memory), and an auxiliary storage device. The ROM is used to store the control program. The RAM is used as a work memory by the CPU. The auxiliary storage device is used to store various data including blood pressure data generated by the blood pressure measurement unit 21 and acceleration data generated by the acceleration sensor 24. The secondary storage device includes, for example, a flash memory. The auxiliary storage device includes a storage medium built in the blood pressure measurement device 20, a removable medium such as a memory card, or both.

The input unit 26 receives an instruction from a person to be measured. The input unit 26 includes, for example, operation buttons, a touch panel, and the like. The output unit 27 is used for outputting information such as a blood pressure measurement result. The output unit 27 includes a display device such as a liquid crystal display device.

The blood pressure measurement device 20 having the above-described configuration outputs measurement data including blood pressure data and acceleration data.

Next, detection of a sudden increase in blood pressure by the blood pressure data processing device 10 according to the present embodiment will be described.

In the first embodiment, the blood pressure data processing device 10 outputs the data 18 of the first peak relating to the sudden increase in blood pressure by processing the time series data 11 of the blood pressure value (the blood pressure value is based on the measurement data acquired from the blood pressure measurement device 20). In the present embodiment, the value of Systolic Blood Pressure (SBP) is used as the time series data 11, but the present invention is not limited to this. As the time series data 11 of the blood pressure value, other values capable of capturing a blood pressure surge may be used. For example, Diastolic Blood Pressure (DBP) and Pulse Pressure (PP) may be used.

The blood pressure data processing apparatus 10 of the present embodiment applies a Sliding Window (Sliding Window) to the time-series data 11 of blood pressure values in pulse units, thereby identifying a peak of a rapid increase in blood pressure. The time-series data 11 does not need to be blood pressure value data in strict units of pulse. In the following description, the term "sliding window" is also referred to as a "window frame", but these terms are used with the same meaning.

The peak of the steep increase in blood pressure output from the blood pressure data processing device 10 of the first embodiment is referred to as a "first peak", and the peak of the steep increase in blood pressure output from the blood pressure data processing device 10 of the second embodiment, which will be described later, is referred to as a "second peak". The difference between the first peak and the second peak will be described in the second embodiment.

Fig. 7 shows an example of a sliding window applied to time series data 11 of blood pressure values. The sliding window SW shown in the figure moves (slides) in units of pulses along the time axis. The movement amplitude on the time axis corresponds to, for example, one pulse. In addition, the sliding window SW has a certain window width Ws along the time axis. The window width Ws corresponds to a length of fifteen pulses, for example. The window width Ws corresponds to the length of the peak detection section when the peak candidate of the blood pressure value is extracted for each moving sliding window SW. Fig. 7 shows a waveform of time-series data 11 of blood pressure values included in the sliding window SW at a certain point in time. The sliding window SW determines whether or not a part of the time series data 11 is a sudden increase in blood pressure based on the feature amount of the blood pressure value.

The feature amount is, for example, a difference F between a point P (also referred to as a "maximum point") in the sliding window SW, which is assigned a maximum value of the SBP, and a point B (also referred to as a "minimum point") in the sliding window SW, which is assigned a minimum value of the SBP at a time point before the point P. Such a difference F corresponds to the amount of variation of SBP in the sliding window SW. Note that the feature amount is not limited to the SBP fluctuation amount. If the feature amount is calculated for the sliding window SW, it is determined whether the feature amount satisfies a determination criterion.

As a determination criterion, a value that can be compared with the difference F of the SBP may be used. For example, the criterion is 20[ mmHg ]. The determination reference value is not limited to this value. For example, the criterion may be set to 15[ mmHg ]. When the determination criterion is satisfied, at least the time of point P (i.e., the peak time of the sharp increase) is held as the determination result. The determination result may include not only the peak time but also the start time of the surge, the end time of the surge, the SBP at the peak, and other feature quantities.

The determination results for the respective sliding windows SW are stored in the memory as peak candidates for each peak detection section. The determination results at the respective points in time of the sliding window SW that moves in the time axis direction, that is, the peak candidates for each peak detection section are merged, and at least one first peak is determined. Specifically, if a certain number of peak candidates or more are obtained at the same time, the time is set as the time of the first peak. It can be considered that each sliding window SW outputs the same peak value around the peak value.

Here, the predetermined number is, for example, "5". In the present embodiment in which the sliding window SW is moved in units of one pulse using time-series data in units of pulses, the predetermined number is referred to as "integrated pulse". The integrated pulse is not limited to 5, and may be appropriately determined in consideration of the detection accuracy of the peak and the processing speed.

The above-described process using the sliding window SW may be modified as follows.

For example, the maximum point of the SBP is taken as a peak candidate. In this case, the processing for comparing the SBP fluctuation amount with the criterion is not performed in each processing accompanying the sliding of the sliding window SW, and the maximum point of the SBP is directly set as the peak candidate. Finally, the first peak is determined by combining the maximum points of SBP for each sliding window SW with the combined pulse number.

The configuration of the blood pressure data processing device 10 according to the first embodiment will be described below.

As shown in fig. 1, the blood pressure data processing device 10 includes a preprocessing unit 12, a peak detection interval setting unit 13, a feature amount calculation unit 14, a peak candidate extraction unit 15, a first peak determination unit 16, and a data output unit 17. Note that, in the case where the maximum point of the SBP is directly set as the peak candidate without performing the comparison with the determination criterion as in the above-described modification, the peak candidate extracting unit 15 can be omitted from the components. That is, the feature value calculation unit 14 outputs the peak candidate.

The blood pressure data processing device 10 holds time series data 11 of blood pressure values (the blood pressure values are based on measurement data acquired from the blood pressure measurement device 20). The time series data 11 of the blood pressure values may be supplied from the blood pressure measurement device 20 to the blood pressure data processing device 10 via a removable medium. Alternatively, the time series data 11 of the blood pressure values may be provided from the blood pressure measurement device 20 to the blood pressure data processing device 10 by communication (wired communication or wireless communication).

The preprocessing unit 12 performs preprocessing such as smoothing using shift averaging, noise removal, and high-frequency component removal using low-pass filtering on the time series data 11 of the blood pressure values acquired from the blood pressure measurement device 20.

The peak detection interval setting unit 13 sets a peak detection interval in the time-series data 11 subjected to preprocessing by the preprocessing unit 12.

The feature value calculation unit 14 calculates a feature value based on any one of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Pulse Pressure (PP) in the peak detection interval set by the peak detection interval setting unit 13. The feature amount calculation unit 14 calculates, for example, a difference F between a point P in the sliding window SW at which the maximum value of the SBP is assigned and a point B in the sliding window SW at a time point before the point P at which the minimum value of the SBP is assigned, as a feature amount.

The peak candidate extraction unit 15 extracts peak candidates for each peak detection section by applying a determination criterion to the feature amount calculated by the feature amount calculation unit 14. Note that, in the case where the comparison between the feature amount (variation) and the determination criterion is not performed as in the above-described modification, the peak candidate extraction unit 15 may not perform any processing.

When the peak candidate extracting unit 15 extracts peak candidates for each peak detection section, the first peak determining unit 16 determines at least one first peak from the peak candidates. If, for example, 5 or more peak candidates are obtained at the same time, the first peak determining unit 16 sets the time as the time of the first peak.

The data output unit 17 outputs the data 18 of the first peak determined by the first peak determining unit 16. The data 18 of the first peak includes: the time of the first peak; and the blood pressure value of the first peak at that time (in the present embodiment, the SBP value).

Next, the operation of the blood pressure data processing device 10 according to the first embodiment will be described. Fig. 8 is a flowchart showing an example of a processing procedure for outputting data of the first peak.

In step S1, the preprocessing unit 12 performs preprocessing such as smoothing using shift averaging, noise removal, and high-frequency component removal using low-pass filtering on the time series data 11 of the blood pressure values acquired from the blood pressure measurement device 20.

Fig. 9 shows an example of removing spike noise, which is one type of noise removal. Spike noise is sometimes included in the time-series data 11 of blood pressure values. In spike noise removal, the height h of the spike is removed _sLarge and small peak-to-peak difference ds. For example, h is removed from the time-series data 11_s≥13[mmHg]And satisfy d_s≤7[mmHg]The blood pressure value of (1). In the left example of fig. 9, white circles indicate 1-point peak noise, which is a blood pressure value of the removal target. Example on the right side of FIG. 9In the middle, white circles indicate 2-point peak noise, which is a blood pressure value of the subject to be removed. Note that a waveform obtained by vertically inverting the waveform of the spike noise shown in fig. 9 may be a target of removal of the spike noise. The data point from which the blood pressure value is removed may be given an interpolation value calculated based on the blood pressure values of the preceding and following data points.

Fig. 10 shows an example of removing a greatly varying noise. For some reason other than the rapid increase in blood pressure, the time series data 11 of blood pressure values may include noise in which the blood pressure values greatly fluctuate. In the large fluctuation noise removal, the difference h between the blood pressure values before and after the pulse_LIf the blood pressure value is equal to or greater than a certain value, the blood pressure value is removed from the time-series data 11. For example, the variation amount is satisfied with h_L≥20[mmHg]The blood pressure value of the condition (2) is removed from the time series data 11 as a large fluctuation noise. In the left example of fig. 10, the white circles indicate the removal target when the blood pressure value is in a downward trend, and in the right example of fig. 10, the white circles indicate the removal target when the blood pressure value is in an upward trend. The data point from which the blood pressure value is removed may be given an interpolation value calculated based on the blood pressure values of the preceding and following data points.

Then, the repetitive processing of each window frame is performed. The window frame is moved along the time axis in pulse units. In step S2, the time when the fluctuation amount in the window frame exceeds the determination criterion is maintained. Specifically, the feature value calculation unit 14 calculates the feature value based on any one of the systolic blood pressure, the diastolic blood pressure, and the pulse pressure in the peak detection interval set by the peak detection interval setting unit 13. When the feature amount exceeds a determination criterion (here, 20 mmHg), the peak candidate extraction unit 15 holds the time of the maximum point as a peak candidate. Step S2 is repeatedly executed while the window frame is moved along the time axis. The peak detection interval setting unit 13 sets the peak detection interval by shifting the position of the pulse wave from the position of the next pulse wave as the window frame moves (slides). In the time-series data 11, the processing of step S2 is repeatedly executed until the position of the last pulse, and the window frame result data is finally output (step S3).

Next, in order to determine the first peak value, an iterative process is performed that targets the sash result data output through step S3. In step S4, if, for example, 5 or more peak candidates are obtained at the same time in the window frame result data, the first peak determination unit 16 holds the time as the time of the first peak. Step S4 is performed for all the sash result data. Finally, all times (i.e., the first peak) at the same time above the combined pulse are determined.

Next, in step S5, a surge determination is performed. Here, the first peak detection result is screened. The first peak determining section 16 performs screening of the first peak detection result by other feature quantities based on at least one of the waveform shape, time information, and frequency information of the time series data 11. Other characteristic quantities include rise time, fall time, area, correlation coefficient of blood pressure surge.

For example, when two adjacent first peaks are detected and the SBP values of the two peaks are almost the same, the screening may be performed by using the first peak of the one having a larger SBP and not using the first peak of the other. Further, the condition of the minimum point (surge start point) used by the feature amount calculation unit 14 in calculating the feature amount (fluctuation amount) may be strengthened. Specifically, instead of the minimum value of the SBP, a point at which the blood pressure value is stable may be used as the surge starting point. In this case, a case more like a surge can be extracted. Further, a correlation coefficient indicating a tendency of rising from the surge start point to the maximum point may be calculated, and the first peak detection result may be screened based on the calculated correlation coefficient. Specifically, the relationship between the SBP and the time from the surge start point to the maximum point is calculated as a correlation coefficient, and the first peak can be determined as surge when the correlation coefficient exceeds a predetermined threshold, and can be determined as non-surge when the correlation coefficient is lower than the predetermined threshold. Such surge determination may be performed using other obtained SBP and DBP characteristic quantities and a characteristic quantity of a pressure pulse wave (for example, data recorded in 125Hz units).

Then, in step S6, the data output unit 17 outputs the data 18 of the first peak value as the detection result of the rapid increase in blood pressure.

The processing shown in fig. 8 describes the case where the rapid increase is determined by performing the repetitive processing of step S4 for merging peak candidates at the same time after performing the repetitive processing of step S2 for comparing the window frame fluctuation amount with the determination criterion (batch processing), but the rapid increase may be determined by performing real-time processing of these 2 repetitive processing almost at the same time.

Fig. 11 is a flowchart illustrating in detail the repetition process illustrated in fig. 8. In steps S21 to S28, the repetitive processing for each window frame is performed. This processing is processing showing step S2 in fig. 8 in more detail. First, a window frame to be subjected to the current repetitive process, that is, a peak detection section is set (step S21). In the present embodiment, the length of the peak detection section is the same as the width of the window frame, that is, 15 pulses. Next, the maximum point of the maximum value of SBP given in the window frame of the processing target is specified for the time series data 11 of blood pressure values (step S22). Next, in the peak detection section, it is determined whether or not data is present at a time point before the maximum point (step S23). If it is determined that there is data at a time point before the maximum point, the process proceeds to step S24, and if it is determined that there is no data, the process proceeds to step S29.

If there is data at a time point before the maximum point, a minimum point calculation section is set in the current processing target peak detection section (step S24), and the minimum point of the SBP in the section is specified (step S25). Based on the maximum point of the SBP determined in step S22 and the minimum point of the SBP determined in step S25, the amount of fluctuation of the SBP in the processing target window frame is calculated (step S26). The fluctuation amount is represented by SBP (max _ time) -SBP (min _ time), for example. The SBP fluctuation is a fluctuation in the window frame to be processed in the time-series data 11 of blood pressure values.

Next, it is determined whether or not the fluctuation amount calculated in step S26 exceeds 20 mmHg, which is a determination criterion (step S27). If the variation exceeds 20[ mmHg ], the routine proceeds to step S28, and if the variation does not exceed 20[ mmHg ], the routine proceeds to step S29. In step S28, the time of the maximum point of the SBP is held in the memory as the first peak point candidate, and the process returns to step S21. In step S21, the window frame to be processed is updated, that is, the peak detection section is shifted to the next pulse position, and the processing after step S22 is executed.

In the case of the modification in which the maximum SBP point is directly set as the peak candidate without comparison with the above-described criterion, steps S23 to S27 are skipped. Alternatively, the calculation of the variation may be performed in steps S23 to S26, and the determination criterion may be set to a convenient value of 0[ mmHg ] in step S27, thereby forcibly proceeding to step S28.

In step S29, the time is set to be defective. That is, it is determined that the candidate of the first peak point cannot be obtained, and the window frame to be processed is updated to the next window frame.

When the processing is finished to the last sash, the sash result data is output (step S30). The window frame result data includes the value of the SBP of the first peak point candidate and the time of the first peak point candidate.

Next, in step S31 to step S33, the repetitive processing for each of the sash result data is performed. This processing is processing showing step S4 shown in fig. 8 in more detail. Here, it is determined whether or not the first peak point candidate at the same time continues to merge pulses or more (step S31). In the present embodiment, the combined pulse rate is 5. If it is determined that the merged pulse is continued or more, the first peak point candidate is set as the first peak point (step S32). If it is determined in step S31 that the first peak point candidate at the same time does not continue to be merged with the pulse or more, step S32 is skipped and the same process is repeated for the next frame result data. If the processing ends up to the last sash result data, the first peak point data is output (step S33). The data of the first peak point is the data 18 of the first peak shown in fig. 1, and the data of the first peak point includes the value of the SBP of the first peak point and the time of the first peak point.

Fig. 12 is a diagram showing a result of detection of a sudden increase in blood pressure by the blood pressure data processing device 10 according to the first embodiment. In this figure, a case is shown in which a plurality of first peak points P1 to P7 detected by the blood pressure data processing device 10 of the first embodiment are detected as a sudden increase in blood pressure together with the waveform of the time-series data 11 of blood pressure values.

The rapid increase in blood pressure does not necessarily occur periodically, and has various features such as the amount of increase and the time of increase in blood pressure value.

According to the first embodiment described above, the first peak of the blood pressure value can be specified by combining a plurality of peak candidates satisfying the determination criterion with the time series data 11 of the blood pressure value. Therefore, a rapid increase in blood pressure can be detected as the first peak. Further, according to the first embodiment, a rapid blood pressure increase is detected with high accuracy based on the time-series data 11 of blood pressure values in pulse units, and a rapid blood pressure increase that does not occur in a certain period and a rapid blood pressure increase having various patterns can be detected with good stability. The blood pressure surge in which the blood pressure value rises sharply is detected based on the fluctuation amount of the maximum value of the SBP in the peak detection section by setting the feature amount used for surge detection as the difference between the maximum value of the SBP in the peak detection section and the minimum value of the SBP at a time point before the maximum value in the peak detection section.

(second embodiment)

Fig. 13 is a block diagram showing a blood pressure data processing device according to a second embodiment. The blood pressure data processing device 10 according to the second embodiment is a device in which the search unit 30 is added to the components of the blood pressure data processing device 10 according to the first embodiment. The searching section 30 includes a peak detecting section 31 before the first peak, a peak detecting section 32 after the first peak, a blood pressure surge determining section 33, and a data output section 34.

The searching unit 30 searches for a second peak (which corresponds to a rapid increase in blood pressure) in the time series data 11 indicating the first peak. As a result of the search process, data 35 of the second peak is output.

In the first embodiment, data 18 of the first peak is output from the time-series data 11 of the blood pressure value. Specifically, the fluctuation amount of SBP is calculated for each frame using a sliding window for the time series data 11, and compared with the criterion for blood pressure surge, and the first peak is determined by integrating a plurality of determination results (including candidates for the first peak for each frame), and at least one data 18 of the first peak is output.

On the other hand, in the second embodiment, the search unit 30 detects at least one second peak by searching the time-series data 11 of blood pressure values for the maximum value of the blood pressure value data at least one time point before and after the search range including the first peak. According to the second embodiment, by performing the search for the maximum value to detect more peaks than in the case where only the first peak is specified, it is possible to detect a sudden increase in blood pressure as the second peak at a time point before the first peak or as the second peak at a time point after the first peak.

The operation of the blood pressure data processing device 10 according to the second embodiment will be described. Fig. 14 is a flowchart showing an example of a processing procedure for outputting data of the second peak.

In step S100, the search unit 30 acquires the data 18, which is the detection result of the first peak. Preferably, the width of the window frame used in the detection of the first peak is set to be sufficiently large to be able to detect various types of surge. As shown in fig. 15A, the blood pressure surge is generated over a short time T1 (for example, 10 seconds), as shown in fig. 15B, the blood pressure surge is generated over a long time T2 (for example, 25 seconds), and the patterns of the surge are various, so it is difficult to determine the template for detection. In order to detect a long blood pressure surge and increase the width of the window frame, only one of the surges P1 and P2 shown in fig. 16 is detected when the surges P1 and P2 occur at relatively short time intervals. In the second embodiment, even if the width of the window frame used for detecting the first peak is sufficiently large, the second peak can be detected by searching for the maximum values before and after the first peak.

Next, the search unit 30 performs the repetition process L1 for each detection result of the first peak. In the repeat processing L1, first, in step S101, the search unit 30 sets a range for searching for the second peak for the surge detection point, which is the first peak to be processed in the repeat processing L1 of this time. Next, the pre-first-peak detector 31 executes the repeat process L2. Here, the local maximum value is searched by tracing back from the surge detection point of the processing target to the start point of the search range set in step S101. Specifically, first, in step S102, it is determined whether or not the maximum local maximum value exists at a time point before the sharp point. Fig. 17A shows a search for the maximum value at a time point before the sharp point. A maximum value S2 before the excitation point S1 is searched. In the case where there is no maximum value, the repetition process L2 is eliminated. If it is determined in step S102 that the maximum local maximum value exists, a local minimum value at a time point before the local maximum value is calculated in step S103. Next, in step S104, the blood pressure surge determination unit 33 determines whether or not the difference between the maximum value searched in step S102 and the minimum value calculated in step S103 exceeds the threshold Th. When the blood pressure exceeds the threshold Th, the blood pressure surge determination unit 33 holds the time of the maximum value as a surge time (second peak) (step S105). In the case where the threshold Th is not exceeded, step S105 is skipped and the repetitive process L2 is continued to be performed.

When the repeat processing L2 ends, the peak detector 32 after the first peak executes the repeat processing L3. Here, the local maximum value is searched by advancing along the time axis from the rapid increase detection point of the processing target to the end point of the search range set in step S101. In fig. 17B, the search for the maximum value at the time point after the sharp point is shown. A maximum S2 after the shot point S1 is searched.

Specifically, first, in step S106, it is determined whether or not the minimum value exists at a time point after the surge point. In the case where there is no minimum, the repetition process L3 is eliminated. If it is determined in step S106 that the minimum value exists, a maximum value at a time point after the minimum value is calculated in step S107. Next, in step S108, the blood pressure surge determination unit 33 determines whether or not the difference between the maximum value searched in step S107 and the minimum value calculated in step S106 exceeds the threshold Th. When the blood pressure exceeds the threshold Th, the blood pressure surge determination unit 33 holds the time of the maximum value as a surge time (second peak) (step S109). In the case where the threshold Th is not exceeded, step S109 is skipped and the repetitive process L3 is continued to be performed.

In step S110, the data output unit 34 outputs the data 35 of the second peak as the surge time determined by the blood pressure surge determination unit 33. Therefore, the data 35 of the second peak is additionally output to the data 18 of the first peak (detection result in step S100). The second peak data 35 may include not only the peak time but also the surge start time, the surge end time, the SBP at the peak time, and other feature quantities.

As described above, according to the second embodiment, more peaks can be detected by performing the search for the maximum value than in the case where only the first peak is specified, and a sudden increase in blood pressure can be detected as the second peak at a time point before the first peak or as the second peak at a time point after the first peak. In other words, it is possible to detect a surge that occurs continuously at short time intervals with respect to the width of the window frame.

(third embodiment)

Fig. 18 is a block diagram showing a blood pressure data processing device according to a third embodiment. The third embodiment is configured by adding a visualization unit 41 to the blood pressure data processing device 10 according to the second embodiment, and the visualization unit 41 outputs a visualization file 40 that is a result of detection of a sudden increase in blood pressure. The visualization unit 41 is configured to differentially display the blood pressure surge detected as the first peak in the time-series data 11 and the blood pressure surge detected as the second peak by the search unit 30 according to the second embodiment.

The visualization unit 41 may be added to the configuration of the blood pressure data processing device 10 according to the first embodiment. Since the second peak is not detected in the first embodiment, the visualization unit 41 cannot display the first peak and the second peak separately, but in the normal display, the visualization unit 41 displays the first peak detected as a sudden increase in blood pressure on the time-series data 11.

The visualization unit 41 according to the third embodiment displays only the first peak and the second peak detected as a sudden increase in blood pressure, or displays both the first peak and the second peak on the time-series data 11 without distinguishing them from each other.

Fig. 19 shows an example of the difference display by the visualization unit 41. In the waveform display of the time series data 11 of the blood pressure values, it is displayed that the blood pressure spikes S1, S3, and S4 are spikes detected as the first peak values, and the blood pressure spike S2 is spikes detected as the second peak values by the search processing of the search unit 30. As described in the second embodiment, the width of the window frame applied to the detection of the first peak is set to a width that enables detection of a long surge.

Fig. 20 shows an example of the visualization file 40 outputted from the visualization unit 41. The visualization file 40 includes, as column items, the flare number No., the peak time, the start time, the end time, the peak SBP, and other feature quantities, and includes column items (detailed search) indicating whether or not detected by the search is by a true false value (t (rue)/f (alse)). For example, if "T" is selected in "detailed search" of the visual file 40 and filtering processing is performed, only the surge detected by the search can be extracted.

In such a third embodiment, it is possible to cope with: the intention of the user who is the observer to confirm the detection result of the first peak that occurs over a long period of time, that is, the long blood pressure surge; and the user intends to confirm the intention of the blood pressure surge generated before and after the detailed detection result of the peak value, i.e., the long blood pressure surge and detected as a second peak value by the search; both of which are described below. In the example of fig. 20, the blood pressure spikes S1 to S4 are displayed at the same time, but the display may be switched so that the blood pressure spikes S2 resulting from the search processing are not displayed, or only the blood pressure spikes S2 are displayed on the contrary.

Next, a hardware configuration example of the blood pressure data processing device 10 will be described with reference to fig. 21.

The blood pressure data processing device 10 includes a CPU191, a ROM192, a RAM193, an auxiliary storage device 194, an input device 195, an output device 196, and a transceiver 197, and these devices are connected to each other via a bus system 198. The above-described functions of the blood pressure data processing apparatus 10 can be realized by the CPU191 reading out and executing a program stored in a computer-readable recording medium (the ROM192 and/or the auxiliary storage device 194). The RAM193 is used as a work memory by the CPU 191. The auxiliary storage 194 includes, for example, a Hard Disk Drive (HDD) or a solid state drive (SDD). The auxiliary storage device 194 is used as a storage section for storing the time-series data 11 shown in fig. 1 and the like. The input devices include, for example, a keyboard, a mouse, and a microphone. The output device includes, for example, a display device such as a liquid crystal display device and a speaker. The transceiver 197 transmits and receives signals to and from other computers. For example, the transceiver 197 receives measurement data from the blood pressure measurement device 20.

(other embodiments)

In the first embodiment, the blood pressure data processing device is provided separately from the blood pressure measuring device. In another embodiment, a part or all of the components of the blood pressure data processing device may be provided in the blood pressure measurement device.

The blood pressure measuring device is not limited to a blood pressure measuring device based on a tension measurement method, and may be any type of blood pressure measuring device capable of continuously measuring blood pressure. For example, it is also possible to use: a blood pressure measuring device measures a Pulse wave Transit Time (PTT) which is a Time taken for a Pulse wave to travel through an artery non-invasively, and estimates a blood pressure value (for example, systolic blood pressure) based on the measured Pulse wave Transit Time. In addition, a blood pressure measuring device that optically measures a volume pulse wave may be used. Further, a blood pressure measuring apparatus that non-invasively measures blood pressure using ultrasonic waves may be used.

The blood pressure measurement device 20 is not limited to a wearable device, and may be a stationary device that measures blood pressure with the upper arm of the subject placed on a stationary table. The wearable blood pressure measurement device does not restrict the movement of the subject, but the sensor unit 22 is easily out of the arrangement suitable for measurement.

The peak detection interval setting unit 13 may use acceleration data for setting the peak detection interval in the time series data 11. For example, the processing for detecting the body motion of the subject may be performed based on the acceleration data, and the peak detection interval setting unit 13 may exclude the time interval during which the body motion is detected from the peak detection interval.

The present invention is not limited to the above-described embodiments, and can be embodied by modifying the structural elements in the implementation stage without departing from the gist thereof. In addition, various inventions can be formed by appropriate combinations of a plurality of constituent elements disclosed in the embodiments. For example, some of the components may be deleted from all the components shown in the embodiments. Further, the constituent elements in the different embodiments may be appropriately combined.

In addition, some or all of the above embodiments may be described as follows, but are not limited to the following.

(attached note 1)

A blood pressure data processing device is provided with:

a processor;

a memory coupled to the processor;

the processor is configured to:

acquiring time sequence data of the blood pressure value;

setting one or more peak detection sections in the time series data, and calculating a feature value based on any one of systolic blood pressure, diastolic blood pressure, and pulse pressure for each peak detection section;

And determining at least one first peak value according to the characteristic quantity of each peak value detection interval.

(attached note 2)

A blood pressure data processing method is provided with:

acquiring time series data of the blood pressure value by using at least one processor;

setting, by at least one processor, one or more peak detection sections in the time series data, and calculating a feature value based on any one of systolic blood pressure, diastolic blood pressure, and pulse pressure for each peak detection section; and

determining, by at least one processor, at least one first peak value from the feature quantity of each of the peak detection intervals.

Claims

1. A blood pressure data processing device is provided with:

an acquisition unit that acquires time series data of continuous blood pressure values;

a calculation unit that sets a plurality of peak detection sections having a constant width and moving along a time axis in the time series data, and calculates a feature value including a maximum value for each of the plurality of peak detection sections, the feature value being based on any one of systolic blood pressure, diastolic blood pressure, and pulse pressure;

an extraction unit that extracts a peak candidate for each of the peak detection sections by applying a determination criterion to the feature amount calculated for each of the plurality of peak detection sections; and

The determination unit determines a first peak value based on a predetermined number or more of the peak candidates at the same time point as the time point at which the maximum value of the peak candidate is obtained.

2. The blood pressure data processing apparatus according to claim 1,

the feature amount includes a difference value between the maximum value in the peak detection section and a minimum value of any one of the systolic blood pressure, the diastolic blood pressure, and the pulse pressure at a time point before the maximum value in the peak detection section.

3. The blood pressure data processing apparatus according to claim 1 or 2,

the determination unit performs the screening of the first peak value based on another feature amount based on at least one of a waveform shape, time information, and frequency information of the time series data.

4. The blood pressure data processing device according to claim 3,

the other characteristic quantities comprise rising time, falling time, area and correlation coefficient of blood pressure surge.

5. The blood pressure data processing apparatus according to claim 1 or 2,

the display unit is further provided for displaying the time-series data together with the first peak value.

6. The blood pressure data processing apparatus according to claim 1 or 2,

the search unit detects at least one second peak by searching for a maximum value of the time series data at least one time point before and after a search range including the first peak.

7. The blood pressure data processing device according to claim 6, further comprising:

a display unit that displays the time-series data together with the first peak value and the second peak value; and

and a display control unit that controls the display unit to display the first peak and the second peak in a differentiated manner.

8. A computer-readable storage medium on which a program for causing a computer to function as the blood pressure data processing apparatus according to any one of claims 1 to 7 is stored.